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Design Process of a Self Adaptive Smart Serious Games Ecosystem

X. Tao, P. Chen, M. Tsami, F. Khayati, M. Eckert

TL;DR

Blexer v3 proposes a modular, AI-driven rehabilitation ecosystem that decouples user state inference from game-specific logic, enabling context-aware, real-time adaptation across multiple serious games. The architecture comprises a Sensor Module, Emotion Module, Context Awareness Module (CAM), and Intelligent Play Module (IPM), connected by layered multimodal data fusion and a CAM-IPM interface. Key innovations include a centralized CAM for high-level state reasoning and a universal IPM as the executable actuator, combined with PCG to continuously tailor content. The framework aims to improve engagement and therapeutic outcomes by integrating DDA with robust sensing, transparent decision-making, and scalable cross-game adaptation, with initial prototypes and planned RL-based refinements. The work advances rehabilitation by enabling personalized, interpretable, and scalable intelligent interventions in home and clinical settings.

Abstract

This paper outlines the design vision and planned evolution of Blexer v3, a modular and AI-driven rehabilitation ecosystem based on serious games. Building on insights from previous versions of the system, we propose a new architecture that aims to integrate multimodal sensing, real-time reasoning, and intelligent control. The envisioned system will include distinct modules for data collection, user state inference, and gameplay adaptation. Key features such as dynamic difficulty adjustment (DDA) and procedural content generation (PCG) are also considered to support personalized interventions. We present the complete conceptual framework of Blexer v3, which defines the modular structure and data flow of the system. This serves as the foundation for the next phase: the development of a functional prototype and its integration into clinical rehabilitation scenarios.

Design Process of a Self Adaptive Smart Serious Games Ecosystem

TL;DR

Blexer v3 proposes a modular, AI-driven rehabilitation ecosystem that decouples user state inference from game-specific logic, enabling context-aware, real-time adaptation across multiple serious games. The architecture comprises a Sensor Module, Emotion Module, Context Awareness Module (CAM), and Intelligent Play Module (IPM), connected by layered multimodal data fusion and a CAM-IPM interface. Key innovations include a centralized CAM for high-level state reasoning and a universal IPM as the executable actuator, combined with PCG to continuously tailor content. The framework aims to improve engagement and therapeutic outcomes by integrating DDA with robust sensing, transparent decision-making, and scalable cross-game adaptation, with initial prototypes and planned RL-based refinements. The work advances rehabilitation by enabling personalized, interpretable, and scalable intelligent interventions in home and clinical settings.

Abstract

This paper outlines the design vision and planned evolution of Blexer v3, a modular and AI-driven rehabilitation ecosystem based on serious games. Building on insights from previous versions of the system, we propose a new architecture that aims to integrate multimodal sensing, real-time reasoning, and intelligent control. The envisioned system will include distinct modules for data collection, user state inference, and gameplay adaptation. Key features such as dynamic difficulty adjustment (DDA) and procedural content generation (PCG) are also considered to support personalized interventions. We present the complete conceptual framework of Blexer v3, which defines the modular structure and data flow of the system. This serves as the foundation for the next phase: the development of a functional prototype and its integration into clinical rehabilitation scenarios.

Paper Structure

This paper contains 19 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. DDA
  3. Foundations and Motivation
  4. Classification and Implementation
  5. Current state of art of difficulty adaptation
  6. Background
  7. Blexer v1: Web-supervised rehabilitation with fixed parameters
  8. Blexer v2: Performance-based personalisation via manual adjustments
  9. Current work on Blexer v3: Context-aware and AI-driven adaptation
  10. Sensor module
  11. Heart Rate Monitoring
  12. Emotion Module
  13. Multimodal Data Fusion
  14. Context Awareness Module (CAM)
  15. Intelligent Play Module (IPM)
  16. ...and 4 more sections

Figures (5)

  • Figure 1: Architecture of the Blexer ecosystem version 2
  • Figure 2: Modules added to middleware and games to compound the new architecture of the Blexer ecosystem version 3
  • Figure 3: System Architecture
  • Figure 4: Emotion classes adopted in our FER model, visualization on the Russell circumflex model Russell
  • Figure 5: Data flow of the Context Awareness Module (CAM)